Sensor Fusion
Showing 36 of 65 projects
An interactive Jupyter Notebook book teaching Kalman and Bayesian filters through Python code and practical examples.
An open-source simulator built on Unreal Engine for developing, training, and validating autonomous driving systems.
An open-source project for developing autonomous vehicle software with datasets, models, and ROS components.
A real-time, tightly-coupled lidar-inertial odometry package for robust robot localization and mapping.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
An optimization-based multi-sensor state estimator for accurate self-localization in drones, cars, and AR/VR applications.
A multi-sensor calibration toolbox for autonomous driving, supporting IMU, LiDAR, camera, and radar calibration.
An open research-oriented C++ framework for multi-session and multi-robot visual-inertial mapping and localization.
An efficient probabilistic 3D mapping framework based on octrees for robotics and computer vision applications.
A ROS package for real-time 6DOF SLAM using 3D LIDAR, featuring graph-based optimization with multiple sensor constraints.
A ROS package providing nonlinear state estimation nodes for robot localization using sensor fusion.
A ROS package for robot-centric elevation mapping that handles pose uncertainty for navigation on rough terrain.
A ROS package for extrinsic calibration between LiDAR and camera sensors using 3D-3D point correspondences.
An open-source GNSS/INS simulation tool that generates sensor data, runs navigation algorithms, and visualizes results for inertial navigation systems.
A target-less, automatic toolbox for LiDAR-camera extrinsic calibration that works with various sensor models without requiring calibration targets.
Fast and robust algorithm for segmenting Velodyne LiDAR point clouds into objects for autonomous driving applications.
A curated list of awesome LIDAR sensors, datasets, libraries, algorithms, and simulators for robotics and autonomous driving.
A curated list of awesome LIDAR sensors, datasets, libraries, algorithms, frameworks, and simulators for robotics and autonomous driving.
A curated collection of papers, toolboxes, and notes for LiDAR-camera extrinsic calibration methods.
ROS tools for IMU devices including orientation filters and visualization plugins.
A ROS-based method for extrinsic calibration between a 3D LiDAR and a 6-DOF pose sensor using point cloud crispness optimization.
A tightly coupled 3D LiDAR-inertial odometry and mapping system for real-time robot localization and mapping.
A deep learning-enhanced Kalman filter for accurate vehicle dead reckoning using only an IMU sensor.
A modular C++ and ROS 2 framework for building configurable LiDAR odometry and SLAM pipelines.
A Python API for the Argoverse dataset, providing tools for 3D tracking, motion forecasting, and HD map interaction for autonomous vehicle research.
An open-source visual-inertial odometry system that estimates camera motion and sparse 3D maps from camera and IMU data.
A ROS framework for sensor fusion using nonlinear least squares optimization, enabling state estimation, localization, mapping, and calibration on robots.
A ROS voxel layer using OpenVDB for efficient 3D environment representation with temporal decay, replacing voxel_grid for navigation.
A C++ library for fast ground segmentation from LiDAR point clouds using the line-fit algorithm.
A lightweight, accurate, and robust monocular visual-inertial odometry system based on a hybrid Multi-State Constraint Kalman Filter.
Open-source software for precise vehicle localization using GNSS and IMU data fusion.
Real-time 3D semantic reconstruction library for robotics, building dense metric-semantic maps from 2D sensor data.
A benchmark dataset for long-range (up to 250m) dense depth estimation in autonomous driving, featuring 360° LiDAR ground truth.
A collection of code samples, unofficial FAQ, and index to supported modules for MicroPython development.
A collection of code samples, unofficial FAQ, and module index for MicroPython, covering hardware drivers, asyncio, GUIs, and embedded systems.
A robust system for multi-LiDAR extrinsic calibration, real-time odometry, and mapping without manual intervention.
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